library(ggplot2)
library(plyr)
library(plotly)
library(ggmap)
df <- read.csv("data/sample_data.csv")
ggplot(df, aes(zipcode, city_id)) +
geom_point() +
geom_jitter() +
labs(title = "Zipcode and City Id ", x = "zipcode", y = "city id")
mysample <- df[sample(1:nrow(df), 10, replace=FALSE),]
ggplot(mysample, aes(zipcode, city_id)) +
geom_text(aes(label = day_of_week), size = 2) +
labs(title = "Zipcode and City Id ", x = "zipcode", y = "city id")
ggplot(df, aes(day_of_week)) +
geom_bar() +
labs(title = "Collision Counts by Day of Week")
computed_df <- ddply(df, .(day_of_week), nrow)
The average salary for different years of service.
ggplot(computed_df, aes(day_of_week, V1)) +
geom_line(stat="identity", color = "blue") +
labs(title = "Collision Counts by Day of Week", x = "day", y = "count")
ggplot(computed_df, aes(day_of_week, V1)) +
geom_area() +
labs(title = "Collision Counts by Day of Week", x = "day", y = "count")
ggplot(df, aes(x = day_of_week)) +
geom_dotplot(binwidth = 0.05, method = "histodot") +
labs(title = "Collision Counts by Day of Week", x = "day", y = "count")
ggplot(df, aes(x = day_of_week)) +
geom_histogram(binwidth = 1) +
labs(title = "Collision Counts by Day of Week", x = "day", y = "count")
ggplot(df, aes(x = longitude)) +
geom_freqpoly(color = "blue") +
labs(title = "Collision Counts by longitude", x = "longitude", y = "count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(df, aes(city, day_of_week)) +
geom_boxplot() +
labs(title = "Distribution about Collision between Day of Week and City", x = "city", y = "day") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(df, aes(city, day_of_week)) +
geom_violin() +
labs(title = "Distribution about Collision between Day of Week and City", x = "city", y = "day") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(df, aes(city_id, event_id)) +
geom_point() +
facet_grid(cols = vars(day_of_week)) +
theme(legend.position="none") +
labs(title = "City Id and Event Id in different days", y = "event id", x = "city id")
ggplot(df,aes(zipcode, city_id, color = day_of_week)) +
geom_point() +
geom_jitter() +
labs(title = "Zipcode and City Id ", x = "zipcode", y = "city id")
ggplotly()
map <- read.csv("data/config.csv")
map
bb <- c(left=-118.5482, bottom=33.6415, right=-118.0846, top=34.2130)
la_county <- get_stamenmap(bbox=bb, maptype="toner")
ggmap(la_county) +
geom_point(data=map, aes(x=longitude, y=latitude), color="red", size=0.5) +
labs(x="Longitude", y="Latitude", title="Distribution of Censors")
## Warning: Removed 2678 rows containing missing values (geom_point).